A Language Detection model can identify the language(s) within a given text or spoken utterances. We discuss the latter, i.e. Spoken Language Detection, in this article.
Language Detection and Language Identification are different terms for the same technology.
Language Detection Use Cases
Polyglot Voice Interfaces that can interact in multiple languages are a compelling use case. Think of a vending machine in an international airport that understands many languages. Code-switching for Automatic Speech Transcription (ASR) is another. Call centres have to keep a record of conversations. In a bilingual (e.g. Canada) or multilingual (e.g. Switzerland) geography, Language Detection can automatically detect the conversation's dialect and pass it to the correct speech-to-text engine. Finally, one might be interested in identifying the language to perform Voice Analytics.
Building a Language Detection Model
Data
First, you need data. Lots of multilingual data. Where to get it? Mozilla Common Voice (CV) and Multilingual LibriSpeech (MLS) are great starting points. This step needs careful and diligent handling. CV and MLS are unbalanced (skewed) in language, gender, and age. Depending on the target application, you need to sample the data nonuniformly.
Model
Work backwards from the requirements. Do you need real-time results? The answer will force your choice of model. If real-time processing is a must, RNN and its more sophisticated variations (GRU, LSTM, SRU, etc.) are feasible choices. Otherwise, you can achieve higher accuracy by adapting a BLSTM or a Transformer (Conformer). Keep in mind real-time doesn’t mean zero latency. It means a guaranteed (constant-ish) latency. Hence you might adapt offline models like BLSTM to process in chunks. Latency-controlled BLSTM (LC-BLSTM) is an example of this approach and is popular in streaming speech-to-text.
Loss
Do you need to be able to detect multiple concurrent languages? E.g. in India, it is common to mix Hindi and English. If yes, what is the granularity? Seconds? Milliseconds? Do you need a binary response, or do you need a confidence metric as well? The loss function can be as simple as a softmax trained with cross-entropy, but depending on requirements can get complex fast.
Besides code-switching, using multiple languages concurrently, communication style (acronyms and slang), and input diversity are other major challenges in language detection.
Building a production language detection model means collecting balanced multilingual data, retraining as languages are added, and maintaining the pipeline indefinitely. If detection is a means to an end rather than your product, an off-the-shelf engine is the faster path: Bat Spoken Language Identification reaches 92.9% accuracy on VoxLingua107 versus 85.0% for SpeechBrain and runs on-device in 5.4 MB of peak memory.
Experts at Picovoice work with Enterprise customers to create private AI algorithms, such as Sound Detection or Speech Emotion Detection, specific to their use cases.
Talk to SalesFrequently Asked Questions
Large, balanced multilingual speech corpora. Mozilla Common Voice and Multilingual LibriSpeech are the usual starting points, but both are skewed by language, gender, and age, so you need to resample to match your target application.
Yes, if the architecture is small enough. RNN variants (GRU, LSTM) suit real-time on-device use; Transformer models are more accurate but heavier. Bat Spoken Language Identification runs on-device with 5.4 MB of peak memory at 92.9% accuracy as proven by the open-source language identification benchmark.
Build when language identification is your core product and you need behavior no vendor offers. Buy when it is one stage in a larger pipeline: an engine like Bat ships benchmarked accuracy, runs on-device, and removes the data collection and retraining burden.
